IDS
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)
IDS is Returns the IDs of the inserted vectors.
24 facts·12 predicates·8 sources·4 in dispute
Mostly:rdf:type(8), contains(3), created by(2)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (13)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
createsVariableCreates Variable(2)
- Insert Method
ex:insert-method - Milvus Branch
ex:milvus-branch
hasSubComponentHas Sub Component(2)
- Network Security
ex:network-security - Network Security
ex:network-security
containsContains(1)
- Data
ex:data
hasArgumentHas Argument(1)
- Collection Insert
ex:collection-insert
hasParameterHas Parameter(1)
- Insert
ex:insert
hasReturnValueHas Return Value(1)
- Insert Vectors
ex:insert_vectors
includesToolIncludes Tool(1)
- Network Security
ex:network-security
insertsDataInserts Data(1)
- Collection Insert
ex:collection-insert
returnsReturns(1)
- Insert Vectors
ex:insert_vectors
takesParameterTakes Parameter(1)
- Self Collection Insert
ex:self-collection-insert
usesUses(1)
- Data Ingestion
ex:data-ingestion
Other facts (22)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
22 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Variable | [2] |
| Rdf:type | Array | [3] |
| Rdf:type | Variable | [3] |
| Rdf:type | Array | [4] |
| Rdf:type | Security System | [5] |
| Rdf:type | Security Tool | [6] |
| Rdf:type | Integer Sequence | [7] |
| Rdf:type | List | [8] |
| Contains | 1 | [4] |
| Contains | 2 | [4] |
| Contains | 3 | [4] |
| Created by | List Comprehension | [1] |
| Created by | Arange Function | [7] |
| Description | Returns the IDs of the inserted vectors | [2] |
| Returned by | Insert Vectors | [3] |
| Abbreviation | IDS | [5] |
| Has Range | 0-999 | [7] |
| Generated by | Np Arange | [7] |
| Has Element Type | Int64 | [7] |
| Has Length | 1000 | [7] |
| Is Created From | Range Function | [8] |
| Inverse Contains | Data | [8] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
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createdBybeam/6deee081-c9a8-4ef0-b743-a35ef9816a7d
ex:list-comprehension
—
typebeam/adbf517e-1335-405d-8a65-aca63a92c7f3
ex:Variable
—
descriptionbeam/adbf517e-1335-405d-8a65-aca63a92c7f3
Returns the IDs of the inserted vectors
—
typebeam/fc7cf36b-fb78-4d1e-89ff-75395398d5c6
ex:Array
—
returnedBybeam/fc7cf36b-fb78-4d1e-89ff-75395398d5c6
ex:insert_vectors
—
typebeam/fc7cf36b-fb78-4d1e-89ff-75395398d5c6
ex:Variable
—
typebeam/68521a31-659b-4aec-9953-6296ab6ed197
ex:Array
—
containsbeam/68521a31-659b-4aec-9953-6296ab6ed197
1
—
containsbeam/68521a31-659b-4aec-9953-6296ab6ed197
2
—
containsbeam/68521a31-659b-4aec-9953-6296ab6ed197
3
—
typebeam/5b9a11ca-e876-4d81-8767-a5dd1674b4d6
ex:SecuritySystem
—
labelbeam/5b9a11ca-e876-4d81-8767-a5dd1674b4d6
Intrusion Detection Systems
—
abbreviationbeam/5b9a11ca-e876-4d81-8767-a5dd1674b4d6
IDS
—
typebeam/6d658107-d832-45d9-b32c-d2ee09ed945c
ex:SecurityTool
—
labelbeam/6d658107-d832-45d9-b32c-d2ee09ed945c
IDS
—
typebeam/d3060ac4-5d8b-4c26-9520-70ab56f38813
ex:IntegerSequence
—
hasRangebeam/d3060ac4-5d8b-4c26-9520-70ab56f38813
0-999
—
createdBybeam/d3060ac4-5d8b-4c26-9520-70ab56f38813
ex:arange-function
—
generatedBybeam/d3060ac4-5d8b-4c26-9520-70ab56f38813
ex:np-arange
—
hasElementTypebeam/d3060ac4-5d8b-4c26-9520-70ab56f38813
ex:int64
—
hasLengthbeam/d3060ac4-5d8b-4c26-9520-70ab56f38813
1000
—
isCreatedFrombeam/926f1488-328b-43c2-9fba-d5492a192351
ex:range-function
—
typebeam/926f1488-328b-43c2-9fba-d5492a192351
ex:List
—
inverseContainsbeam/926f1488-328b-43c2-9fba-d5492a192351
ex:data
References (8)
8 references
ctx:claims/beam/6deee081-c9a8-4ef0-b743-a35ef9816a7d- full textbeam-chunktext/plain1 KB
doc:beam/6deee081-c9a8-4ef0-b743-a35ef9816a7dShow excerpt
vectors = np.random.rand(num_vectors, 128).astype('float32').tolist() ids = [str(i) for i in range(num_vectors)] start_time = time.time() self.collection.insert(vectors, ids) end_t…
ctx:claims/beam/adbf517e-1335-405d-8a65-aca63a92c7f3- full textbeam-chunktext/plain1 KB
doc:beam/adbf517e-1335-405d-8a65-aca63a92c7f3Show excerpt
# Perform search results = search(COLLECTION_NAME, query_vector, TOP_K) print(results) ``` ### Explanation 1. **Collection Creation**: - `create_collection`: Creates a collection with specified parameters, including dimensi…
ctx:claims/beam/fc7cf36b-fb78-4d1e-89ff-75395398d5c6- full textbeam-chunktext/plain1 KB
doc:beam/fc7cf36b-fb78-4d1e-89ff-75395398d5c6Show excerpt
"dimension": dimension, "index_file_size": 1024, # Size of each segment file in MB "metric_type": METRIC_TYPE } milvus.create_collection(param) # Create an index def create_index(name, index_type, nlist): …
ctx:claims/beam/68521a31-659b-4aec-9953-6296ab6ed197ctx:claims/beam/5b9a11ca-e876-4d81-8767-a5dd1674b4d6- full textbeam-chunktext/plain1 KB
doc:beam/5b9a11ca-e876-4d81-8767-a5dd1674b4d6Show excerpt
[Turn 3712] User: I'm trying to estimate the effort required to finalize 70% of the security architecture, and I've allocated 12 hours for this task, but I'm not sure if it's enough ->-> 9,19 [Turn 3713] Assistant: Estimating the effort re…
ctx:claims/beam/6d658107-d832-45d9-b32c-d2ee09ed945cctx:claims/beam/d3060ac4-5d8b-4c26-9520-70ab56f38813- full textbeam-chunktext/plain1 KB
doc:beam/d3060ac4-5d8b-4c26-9520-70ab56f38813Show excerpt
[Turn 4944] User: I'm spending 6 hours on Milvus tutorials to improve my database skills, targeting a 20% knowledge increase. As part of this, I want to practice designing an efficient vector indexing workflow using Milvus. Can you guide me…
ctx:claims/beam/926f1488-328b-43c2-9fba-d5492a192351- full textbeam-chunktext/plain1 KB
doc:beam/926f1488-328b-43c2-9fba-d5492a192351Show excerpt
FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=128) ] schema = CollectionSchema(fields, "Document Embeddings") # Create the collection collection = Collection("document_embeddings", schema) ``` #### 3. Insert Vectors …
See also
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